TurboQuant WASM

Experimental WASM + relaxed SIMD build of botirk38/turboquant for browsers and Node.js.

Based on the paper "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate" (Google Research, ICLR 2026).

Live Demo — vector search, image similarity, and 3D Gaussian Splatting compression running in the browser.

Why TurboQuant?

Float32 embedding indexes are large — 1M vectors × 384-dim = 1.5GB. They don't fit in mobile RAM, take minutes to download, and gzip only saves ~7% because float32 has high entropy.

TurboQuant compresses them 6x (1.5GB → 240MB) and searches directly on compressed data without decompressing. No training step — unlike PQ/OPQ, just init({ dim, seed }) and encode any vector immediately.

What this adds

• npm package with embedded WASM — npm install turboquant-wasm
• WebGPU acceleration — dotBatch() auto-detects WebGPU and dispatches a compute shader that scans compressed vectors directly on GPU, no decompression
• CPU fallback — transparent fallback to WASM relaxed SIMD when WebGPU is unavailable
• Relaxed SIMD — @mulAdd FMA maps to f32x4.relaxed_madd
• SIMD-vectorized QJL sign packing/unpacking and scaling
• TypeScript API — TurboQuant.init() / encode() / decode() / dot() / dotBatch()
• Golden-value tests — byte-identical output with the reference Zig implementation

Browser Requirements

The WASM binary uses relaxed SIMD instructions:

| Runtime | Minimum Version | |---------|----------------| | Chrome | 114+ | | Firefox | 128+ | | Safari | 18+ | | Node.js | 20+ |

Quick Start

import { TurboQuant } from "turboquant-wasm";

const tq = await TurboQuant.init({ dim: 1024, seed: 42 });

// Compress a vector (~4.5 bits/dim, ~6x compression)
const compressed = tq.encode(myFloat32Array);

// Decode back
const decoded = tq.decode(compressed);

// Fast dot product without decoding
const score = tq.dot(queryVector, compressed);

// Batch search: auto-uses WebGPU when available, falls back to WASM SIMD
const allCompressed = new Uint8Array(/* concatenated compressed vectors */);
const scores = await tq.dotBatch(queryVector, allCompressed, bytesPerVector);

tq.destroy();

API

class TurboQuant {
  static async init(config: { dim: number; seed: number }): Promise<TurboQuant>;
  encode(vector: Float32Array): Uint8Array;
  decode(compressed: Uint8Array): Float32Array;
  dot(query: Float32Array, compressed: Uint8Array): number;
  dotBatch(query: Float32Array, compressedConcat: Uint8Array, bytesPerVector: number): Promise<Float32Array>;
  rotateQuery(query: Float32Array): Float32Array;
  destroy(): void;
}

dotBatch() transparently uses WebGPU when available (Chrome 113+, Edge 113+). The GPU compute shader reads compressed data directly — no decompression step. Falls back to WASM SIMD on devices without WebGPU.

Building

# Run tests
zig test -target aarch64-macos src/turboquant.zig

# Full npm build (zig -> wasm-opt -> base64 embed -> bun + tsc)
bun run build

# Build WASM only
bun run build:zig

Requires Zig 0.15.2 and Bun.

Quality

Encoding preserves inner products — verified by golden-value tests and distortion bounds:

• MSE decreases with dimension (unit vectors)
• Bits/dim is ~4.5 (payload only, excluding 22-byte header)
• Dot product preservation — mean absolute error < 1.0 for unit vectors at dim=128
• Bit-identical output with botirk38/turboquant for same input + seed

When to use TurboQuant (and when not to)

| | TurboQuant | PQ/OPQ (FAISS, ScaNN) | |---|---|---| | Compression | ~4.5 bits/dim (6x) | ~1-2 bits/dim (16-32x) | | Query speed | Slower (float decode per pair) | Faster (integer codebook lookup) | | Training | None — encode any vector immediately | Required — must train codebook on dataset | | Streaming data | Yes — each vector is self-contained | Degrades if data distribution shifts | | Setup | npm install + 3 lines of code | Dataset-dependent configuration |

Use TurboQuant when: vectors arrive continuously (LLM KV cache, real-time indexing), you can't pause to train, you need simple deployment (browser, edge), or you want a single npm package with no dependencies.

Use PQ/OPQ when: you have a static dataset, can afford a training step, and need maximum compression + fastest query speed. PQ is the better tool for traditional batch vector search.

Credits

• botirk38/turboquant — original Zig implementation
• TurboQuant paper (Google Research, ICLR 2026) — algorithm design

License

MIT